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Dive into Deep Learning Compiler
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Dive into Deep Learning Compiler
Table Of Contents

  * 1. Getting Started
      + 1.1. Installation
      + 1.2. Vector Add
      + 1.3. Neural Network Inference
      + 1.4. Running on a Remote Machine
  * 2. Expressions for Operators
      + 2.1. Data Types
      + 2.2. Shapes
      + 2.3. Index and Shape Expressions
      + 2.4. Reduction Operations
      + 2.5. Conditional Expression: if-then-else
      + 2.6. Truth Value Testing: all and any
  * 3. Common Operators
      + 3.1. Broadcast Add
      + 3.2. Matrix Multiplication
      + 3.3. Convolution
      + 3.4. Depthwise Convolution
      + 3.5. Pooling
      + 3.6. Batch Normalization
  * Operator Optimizations on CPUs
      + 1. CPU Architecture
      + 2. Function Call Overhead
      + 3. Vector Add
      + 4. Broadcast Add
      + 5. Matrix Multiplication
      + 6. Improve Cache Efficiency by Blocking
      + 7. Convolution
      + 8. Packed Convolution
      + 9. Depthwise Convolution
      + 10. Pooling
      + 11. Batch Normalization
  * Operator Optimizations on GPUs
      + 1. GPU Architecture
      + 2. Vector Add
      + 3. Broadcast Add
      + 4. Matrix Multiplication
      + 5. Convolution
      + 6. Depthwise Convolution
      + 7. Pooling
      + 8. Batch Norm
  * 4. Neural Networks
  * 5. Deployment

  * References

Dive into Deep Learning Compiler
Table Of Contents

  * 1. Getting Started
      + 1.1. Installation
      + 1.2. Vector Add
      + 1.3. Neural Network Inference
      + 1.4. Running on a Remote Machine
  * 2. Expressions for Operators
      + 2.1. Data Types
      + 2.2. Shapes
      + 2.3. Index and Shape Expressions
      + 2.4. Reduction Operations
      + 2.5. Conditional Expression: if-then-else
      + 2.6. Truth Value Testing: all and any
  * 3. Common Operators
      + 3.1. Broadcast Add
      + 3.2. Matrix Multiplication
      + 3.3. Convolution
      + 3.4. Depthwise Convolution
      + 3.5. Pooling
      + 3.6. Batch Normalization
  * Operator Optimizations on CPUs
      + 1. CPU Architecture
      + 2. Function Call Overhead
      + 3. Vector Add
      + 4. Broadcast Add
      + 5. Matrix Multiplication
      + 6. Improve Cache Efficiency by Blocking
      + 7. Convolution
      + 8. Packed Convolution
      + 9. Depthwise Convolution
      + 10. Pooling
      + 11. Batch Normalization
  * Operator Optimizations on GPUs
      + 1. GPU Architecture
      + 2. Vector Add
      + 3. Broadcast Add
      + 4. Matrix Multiplication
      + 5. Convolution
      + 6. Depthwise Convolution
      + 7. Pooling
      + 8. Batch Norm
  * 4. Neural Networks
  * 5. Deployment

  * References

Dive into Deep Learning CompilerP

Working in progress. Check our roadmap for more details.

This project is for readers who are interested in high-performance
implementation of their programs utilizing deep learning techniques,
especially model inference, but may not have got their hands dirty
yet. We assume readers have a minimal background of only having
experience on NumPy before. With this in mind, we will explain things
from scratch and introduce relative background when needed.
Experienced readers, however, should also find the contents useful.

We roughly classify contents into three major parts. In the first
part, we will introduce how to implement and optimize operators, such
as matrix multiplication and convolution, for various hardware
platforms. This is the basic component for deep learning as well as
scientific computing in general. In the second part, we will show how
to convert neural network models from various deep learning
frameworks and further optimize them in the program level. The last
part we will address how to deploy the optimized program into various
environment such as mobile phones. In addition, at the end of the
book, we plan to cover some latest advance of the deep learning
compiler domain.

  * 1. Getting Started
      + 1.1. Installation
      + 1.2. Vector Add
      + 1.3. Neural Network Inference
      + 1.4. Running on a Remote Machine
  * 2. Expressions for Operators
      + 2.1. Data Types
      + 2.2. Shapes
      + 2.3. Index and Shape Expressions
      + 2.4. Reduction Operations
      + 2.5. Conditional Expression: if-then-else
      + 2.6. Truth Value Testing: all and any
  * 3. Common Operators
      + 3.1. Broadcast Add
      + 3.2. Matrix Multiplication
      + 3.3. Convolution
      + 3.4. Depthwise Convolution
      + 3.5. Pooling
      + 3.6. Batch Normalization
  * Operator Optimizations on CPUs
      + 1. CPU Architecture
      + 2. Function Call Overhead
      + 3. Vector Add
      + 4. Broadcast Add
      + 5. Matrix Multiplication
      + 6. Improve Cache Efficiency by Blocking
      + 7. Convolution
      + 8. Packed Convolution
      + 9. Depthwise Convolution
      + 10. Pooling
      + 11. Batch Normalization
  * Operator Optimizations on GPUs
      + 1. GPU Architecture
      + 2. Vector Add
      + 3. Broadcast Add
      + 4. Matrix Multiplication
      + 5. Convolution
      + 6. Depthwise Convolution
      + 7. Pooling
      + 8. Batch Norm
  * 4. Neural Networks
  * 5. Deployment

  * References

DiscussionsP

Table Of Contents

  * Dive into Deep Learning Compiler
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1. Getting Started